SOTAVerified

Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

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Showing 351–375 of 1322 papers

TitleStatusHype
Two-Person Interaction Augmentation with Skeleton Priors—0
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network—0
Human Activity Recognition using Smartphones—0
Learning Alternative Ways of Performing a TaskCode0
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels—0
EventSleep: Sleep Activity Recognition with Event Cameras—0
HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional MambaCode0
Multi-channel Time Series Decomposition Network For Generalizable Sensor-Based Activity Recognition—0
Activity-Biometrics: Person Identification from Daily ActivitiesCode0
Emotion Recognition from the perspective of Activity Recognition—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition—0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
Generalized Relevance Learning Grassmann QuantizationCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer—0
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review—0
Knowledge Transfer across Multiple Principal Component Analysis Studies—0
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition—0
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition—0
Cross-user activity recognition via temporal relation optimal transport—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models—0
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks—0
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems—0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified